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Nir Ailon
Researcher at Technion – Israel Institute of Technology
Publications - 105
Citations - 7211
Nir Ailon is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Upper and lower bounds & Fourier transform. The author has an hindex of 32, co-authored 104 publications receiving 6210 citations. Previous affiliations of Nir Ailon include Princeton University & Institute for Advanced Study.
Papers
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Book ChapterDOI
Deep metric learning using Triplet network
Elad Hoffer,Nir Ailon +1 more
TL;DR: This paper proposes the triplet network model, which aims to learn useful representations by distance comparisons, and demonstrates using various datasets that this model learns a better representation than that of its immediate competitor, the Siamese network.
Posted Content
Deep metric learning using Triplet network
Elad Hoffer,Nir Ailon +1 more
TL;DR: In this paper, Wang et al. proposed the triplet network model, which aims to learn useful representations by distance comparisons, and demonstrate using various datasets that their model learns a better representation than that of its immediate competitor, the Siamese network.
Journal ArticleDOI
Aggregating inconsistent information: Ranking and clustering
TL;DR: This work almost settles a long-standing conjecture of Bang-Jensen and Thomassen and shows that unless NP⊆BPP, there is no polynomial time algorithm for the problem of minimum feedback arc set in tournaments.
Journal ArticleDOI
The Fast Johnson-Lindenstrauss Transform and Approximate Nearest Neighbors
Nir Ailon,Bernard Chazelle +1 more
TL;DR: A new low-distortion embedding of $\ell-2^d$ into $\ell_p^{O(\log n)}$ ($p=1,2$) called the fast Johnson-Lindenstrauss transform (FJLT) is introduced, based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform.
Proceedings ArticleDOI
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
Nir Ailon,Bernard Chazelle +1 more
TL;DR: A new low-distortion embedding of l2d into l p (p=1,2) is introduced, called the Fast-Johnson-Linden-strauss-Transform (FJLT), based upon the preconditioning of a sparse projection matrix with a randomized Fourier transform.